The present invention relates to an automated computer-based data integrity auditing system. The first embodiment of the invention is a system for auditing patient assessment data produced by nursing homes. It will be evident, however, that the system has utility for any industry that relies upon standardized databases in the production or evaluation of products or services, as long as those databases have logical and practical relationships among items, and/or discrete scales that are internally consistent, and are correlated with others in a predictable way.
When databases are used to produce or evaluate products and services that are essential to the mission of an industry, validity of data is necessary to ensure the quality of products or services. Such industries include, without limitation, healthcare, financial services, and education. The invention is described in a healthcare context. However, it is evident that the methods described are applicable to other industries. The invention is applicable to any industry which utilizes databases having particular relationships between database elements.
In recent years, both the cost and the potential benefits of healthcare have increased substantially. Consumers of care, and payers for care (if they are other than the consumers), increasingly demand proof of benefits or quality of care to justify the funds they spend on that care. To prove the benefits of care and to evaluate the quality of care, it is necessary to measure processes and outcomes of care using standardized methodology. Standardized measurement permits comparisons over time and among care providers, and may allow for risk adjustment.
Standardized assessment of care outcomes and processes requires measurement of patients' health status and recording of the services they receive. When such data are valid, they also may be used for quality improvement efforts. Decision support algorithms can be based upon such data, and performance measures based upon such data can be fed back to quality improvement teams. Additional uses of standardized health status and health services data include: 1) prediction of care outcomes (prognosis); 2) needs assessment for communities or healthcare institutions; 3) regulatory oversight of healthcare providers; and 4) calculation of fees or reimbursements based on illness severity or service intensity.
Nursing homes in the U.S. certified by the Health Care Financing Administration (HCFA) to receive Medicare or Medicaid reimbursement are subject to a requirement to submit standardized assessment data on every one of their residents. The mandatory database is called the Minimum Data Set (MDS). The MDS comprises over 500 items, mainly checklist and multiple-choice items, dealing with the resident's demographics, baseline functioning and preferences, current health status, and recent and present health services used.
Nursing homes seeking Medicare reimbursement for a resident must perform MDS assessment on or about days 5, 14, 30, 60 and 90 of a resident's admission. Nursing homes seeking Medicaid reimbursement must perform MDS assessments by day 14 after a resident is admitted, and quarterly thereafter. A complete, comprehensive MDS must be submitted based on the resident's status on admission, and must be updated annually thereafter. Medicare residents require complete assessments to be done (i.e., updated) at each follow-up. Medicaid residents must have a less-comprehensive MDS assessment quarterly, and a full reassessment every year. Facilities must transmit the required MDS assessments electronically to designated State agencies, using a mandatory format for electronic data interchange.
Medicare reimbursement for skilled nursing care is determined by applying a classification algorithm to one hundred eight of the MDS items. Based on these items, HCFA and its fiscal intermediaries classify a nursing home resident into one of forty-four Resource Utilization Groups (RUGS). Each RUG is associated with a specific rate of per diem reimbursement.
HCFA also has designated quality indicators (QIs) calculated from MDS data. These QIs are rates of various clinical conditions that may be correlated with quality of care. For example, one QI is the rate of pressure ulcers (bed sores). In general, higher rates of pressure ulcers are associated with less satisfactory care. A more accurate assessment of quality adjusts the QI for various factors that influence the risk of pressure ulcers, e.g., malnutrition and immobility. Even more accurate performance measures are based on the incidence rather than the prevalence of pressure ulcers, or on rates of healing of pre-existing pressure ulcers.
Nursing facilities can use MDS data to manage their legal liability for adverse outcomes of care. In some cases, MDS data concerning risk factors enable a facility to show that a person experiencing a poor outcome was at especially high risk for that outcome. This would argue that the care was not necessarily substandard. In others, a facility can show that its overall performance in a given area of care was superior, so that a particular adverse event represented a chance occurrence rather than the result of a pattern of negligence or misconduct. Advocates and attorneys bringing complaints against nursing homes can use MDS data in similar ways with opposite objectives.
The various purposes of the nursing home MDS, or of any other health status/health service database can only be served if the data are valid. This requires valid assessments, accurate coding of the assessments, and accurate representation of the codes in electronic form whether via manual data entry or via scanning of forms. The requirement for validity is extremely high if data on individual residents are to be used for clinical decision-making or legal purposes. If only aggregated data from entire facilities or units will be analyzed, it may only be necessary that validity exceed a reasonable lower bound.
The “gold standard” of validity testing is independent verification of the data found in the electronic MDS record by a qualified expert who directly examines the resident, interviews caregivers, and reviews all relevant clinical records. This is an expensive process, feasible only for a small sample of residents at any given facility. In some cases, the “gold standard” can never be attained because the status of the resident has changed between the time of the computerized MDS assessment and the time of the validation assessment by the expert.
Therefore, there is a need to:    1) define a reasonable proxy measure for the validity of health status/health services databases;    2) define a way to quantify data validity, and to indicate whether particular data elements are trustworthy;    3) determine the acceptability of data for the different purposes to which it may be put;    4) guide assessors and coders in modifying their processes to systematically improve data quality;    5) ensure higher quality data during the process of assessment, coding, and data entry, before submission of the data for analysis, reimbursement, or other uses; and    6) automate the processes of data quality assessment and improvement to make them economical and feasible for universal application.
The present invention provides an advantageous system that meets the aforementioned needs. In particular, the invention defines a proxy measure, called data integrity, for data validity and describes a system for measuring it.
It will be apparent to one skilled in the art that the methodology of the present invention is applicable not only to various kinds of health status/health service data, but to any standardized data concerning service processes and the status of the individuals or objects to which those processes apply. A simple extension is to home care, for which the Outcome and Statistics Information Set (OASIS), a tool similar to the MDS, is used in regulation and reimbursement. In the financial service industry, data validity tests can be applied to databases with data that include customers' demographics, portfolios, transaction histories, preferences, and satisfaction. In education, data validity tests can be applied to databases of demographics, scores on scholastic aptitude and achievement tests, courses taken and scores given, etc. The system for evaluating MDS data integrity described in detail here should therefore be regarded as just one example of a broadly applicable methodology.